compute.sigma.star: Computes intermediate (tetrachoric) correlation matrix
In BinNor: Simultaneous Generation of Multivariate Binary and Normal Variates

Description Usage Arguments Value See Also Examples

View source: R/compute.sigma.star.R

This function computes the intermediate correlation matrix by combining tetrachoric correlation for binary-binary combinations, biserial correlations for binary-normal combinations and Pearson correlation for normal-normal combinations. If the resulting correlation matrix is not positive definite, a nearest positive matrix will be used.

1 2	compute.sigma.star(no.bin, no.nor, prop.vec.bin = NULL, corr.vec = NULL, corr.mat = NULL)

`no.bin`	Number of binary variables
`no.nor`	Number of normal variables
`prop.vec.bin`	Probability vector for binary variables
`corr.vec`	Vector of elements below the diagonal of correlation matrix ordered columnwise
`corr.mat`	Specified correlation matrix

`sigma_star`	A resulting intermediate correlation matrix Σ^*
`nonPD`	If a resulting intermediate correlation matrix is non-positive definite, it is stored in this value. Otherwise it is NULL.
`PD`	TRUE if Σ^* is positive definite, FALSE otherwise. A FALSE indicates that the nearest positive definite matrix is returned.
`eigenv`	Eigenvalues of the Σ^* before the conversion

validation.corr, nearPD, phi2poly, is.positive.definite,
jointly.generate.binary.normal, simulation

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cmat = lower.tri.to.corr.mat(corr.vec= c(0.16, 0.04, 0.38, 0.14, 0.47, 0.68),4)
compute.sigma.star(no.bin=2, no.nor=2, prop.vec.bin=c(0.4,0.7), 
corr.vec=NULL,corr.mat=cmat)